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Birkhäuser - Birkhäuser Mathematics | Towards an Information Theory of Complex Networks - Statistical Methods and Applications

Towards an Information Theory of Complex Networks

Statistical Methods and Applications

Dehmer, Matthias, Emmert-Streib, Frank, Mehler, Alexander (Eds.)

2011, XVI, 395p. 114 illus..

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  • First book on the market giving a comprehensive look at the applications of information-theoretic models for complex networks
  • Synthesizes graph-theoretic, statistical, and information-theoretic methods to effectively understand and characterize real-world networks
  • Addresses a broad range of disciplines, including quantitative biology, quantitative chemistry, quantitative sociology, and quantitative linguistics
  • Caters to both researchers and scholars across the sciences

For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A  tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks.

This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. It begins with four chapters developing the most significant formal-theoretical issues of network modeling, but the majority of the book is devoted to combining theoretical results with an empirical analysis of real networks. Specific topics include:

  • chemical graph theory
  • ecosystem interaction dynamics
  • social ontologies
  • language networks
  • software systems

This work marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines. As such, it can serve as a valuable resource for a diverse audience of advanced students and professional scientists. It is primarily intended as a reference for research, but could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.

Content Level » Research

Keywords » complexity - data analysis - entropy - information theory - networks

Related subjects » Birkhäuser Computer Science - Birkhäuser Engineering - Birkhäuser Mathematics

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